A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging

Area of Science:

• Neuroscience
• Medical Imaging
• Neurology

Background:

• Understanding Alzheimer's disease (AD) heterogeneity is vital for precision medicine.
• Existing neuroimaging frameworks face challenges in translating AD heterogeneity insights from research to clinical practice.

Purpose of the Study:

• To systematically review and organize data-driven neuroimaging methodologies for Alzheimer's disease heterogeneity.
• To evaluate current approaches based on pathology understanding and clinical applicability.
• To identify limitations and future directions for enhancing AD neuroimaging heterogeneity frameworks.

Main Methods:

• Systematic review of prior studies on data-driven neuroimaging for AD heterogeneity.
• Organization of methodologies into subtyping, stratification, and individual-specific pattern analysis.
• Evaluation of studies based on pathology understanding and clinical application.

Main Results:

• Methodologies were categorized into patient subtyping (cross-sectional and longitudinal), biomarker-integrated stratification, and normative modeling for individual patterns.
• Current studies were assessed for their depth in understanding AD pathology and their clinical utility.
• Key limitations and challenges in the field were identified.

Conclusions:

• Enhanced neuroimaging heterogeneity frameworks are needed to advance precision medicine for Alzheimer's disease.
• Bridging the gap between bench and bedside requires addressing current methodological limitations.
• Future research should focus on refining these frameworks for better clinical translation.